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Pinot is part of the Big Data Platform (BDP) organization at LinkedIn. The Big data platform team builds the infrastructure and platforms that powers much of the analytics and data processing at LinkedIn. The team has a wide span and we solve difficult problems in areas as diverse as storage (HDFS) , Resource management (Yarn), Compute platforms (Spark/Map-Reduce), and Analytics query processing (Pinot/Presto). We’ve built and participated in well regarded open source technology for Data management (Apache Gobblin), distributed OLAP datastore (Apache (Incubating) Pinot), Query engine(Presto) as well as internal frameworks for scheduling (Azkaban) and data abstraction (Dali).
Pinot is a realtime distributed OLAP datastore, built to deliver scalable real time analytics with low latency(10s ms) and serves millions of active users serving 1000s of queries per second. It has established itself as the de-facto analytics data store at LinkedIn powering multiple member facing features like Who viewed my Profile, LinkedIn Feed, Jobs you may be interested in, etc. and also powers multiple LinkedIn products like LinkedIn Talent Insights, LinkedIn Audience Engagement Insights, LinkedIn Sales Insight among others. We develop in the Open Source and have a very healthy OS community to work and interact with (Pinot is an Apache Incubating Project and plans to graduate to a top level project soon).
We are working on making Pinot a truly cloud based service and run it as a PaaS offering to easily build analytics products/applications with ultra low latency and support for both realtime and offline data ingestion. We are also working on adding features like Differential Privacy to our offering to protect our member’s identity and more. Pinot powers a large number of analytics use cases and products – Feed, Who viewed my Profile, LinkedIn Talent Insights, LinkedIn Sales Insight and many others. It requires infra to provide capabilities to onboard new use cases quickly and enable product engineers to build analytics and insight products with minimal friction and for scale. We are looking for a Sr. Staff SWE with strong execution and leadership capabilities to help us deliver our charter of building the next generation of low latency analytics for the cloud. As a Sr. Staff Software Engineer, you will be a key technical leader and a role model within the Analytics Infra organization. In this role, you will be responsible for working with other Big Data Platform engineers, managers, and partners to create the vision, technical roadmap and make it a reality.
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